Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey
by
, ,
Habimana Emmanuel
1,2, 
Jaehyung Yu
1,3,*
,
Lei Wang
4
,
Sung Hi Choi
1,3 and 
Digne Edmond Rwabuhungu Rwatangabo
2 1
Department of Astronomy, Space Science and Geology, Chungnam National University, Daejeon 34134, Republic of Korea
2
School of Mining and Geology, University of Rwanda, Kigali 3900, Rwanda
3
Department of Geological Sciences, Chungnam National University, Daejeon 31134, Republic of Korea
4
Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(15), 3807; https://doi.org/10.3390/rs15153807
Submission received: 11 June 2023
/
Revised: 21 July 2023
/
Accepted: 27 July 2023
/
Published: 31 July 2023
(This article belongs to the Special Issue Quantifying Digital Geomorphology and Planetary Geomorphology Using Remote Sensing Techniques)
Abstract
:The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.
1. Introduction
Impact craters are important geological features in planetary sciences formed by comets or asteroids colliding with the planetary surface [1]. The circular or sub-circular topographic feature represents the energy level of the meteorite impact, which can be observed by satellites [2]. Impact craters have been one of the major scientific focuses in the science community because they provide evidence for inference of planet of evolution and understanding of space materials [3]. Moreover, they are a critical indicator related to space hazards inducing secondary earthquakes and tsunamis [4]. Estimating impact crater ages assist in quantifying impact flux in the geological time scale and relates to other geological events in Earth’s history [5]. Therefore, geoscientists have made substantial endeavors to detect and describe impact crater landscapes based on various sources such as remote sensing, geophysics, and geological data.
The number of confirmed craters is biased by the disparity in survey technology because most confirmed craters are located in developed countries [6]. Kenkmann et al. [7] reported 198 confirmed and 10 suspected impact craters unevenly distributed worldwide, with 65 in North America, 54 in Europe, 31 in Australia, 23 in Asia, 21 in Africa, and 14 in South America [7]. The regional differences indicate that the number of impact craters in developing countries should be higher than the current reports, and more cases could be identified in those low-finding areas.
Identification of terrestrial impact craters usually requires two major steps. The first step is the preliminary identification of crater candidates through remote sensing, including topographic, optical, gravity, or magnetic surveys, and the second step is a conclusive investigation by intensive geological surveys, including well-logging, lab analysis, geochemical analysis, and shock metamorphic experiments. These confirm the impact craters with ultimate evidence (shatter cones and planar deformation features, etc.) [8,9,10]. Remote sensing can provide worldwide coverage of preliminary assessment of possible crater locations to narrow down candidates using satellite data [11,12]. In addition, geophysical surveys play a major role in the identification of deeply eroded or buried suspected impact structures [13,14].
The topographic features from Digital Elevation Models (DEMs) can be used to identify possible candidates based on geomorphological characteristics such as crater rims and ring structures [9,15]. DEMs also can be used to define geomorphological parameters of confirmed caters from sophisticated geological surveys [16,17,18]. Additionally, the drainage networks associated with impact structures are also analyzed to trace the extent of the drainage basin [15]. The drainage network within a preserved impact crater shows drainage patterns such as centripetal or centrifugal radial drainage and sometimes concentric drainage where crater rims act as catchment [7,15].
Multispectral satellite data have been used to derive structure features and lithological discontinuities that might be related to impact cratering [11,15]. Linear features extracted from Landsat satellite data revealed the orientation of linear geological structures and distribution of lithological units, such as ejecta of brecciated materials and shock-induced alterations [15,19]. Radial and annular lineament patterns have been observed in several exposed impact structures as a result of concentric depressions and rim escarpments [9].
Furthermore, automatic computer algorithms have been used to detect impact craters based on the circular morphometric resemblance of impact structures displayed in remote sensing data [20]. Various Crater Detection Algorithms (CDAs) have been used on extraterrestrial impact craters such as lunar and Mars craters [21,22]. However, limited attempts have been made for the automatic detection of terrestrial craters [23]. Masaitis et al. [24] and Kenkmann et al. [7] highlighted that complex geological processes and size variations of terrestrial impact craters would hinder the automatic detection of terrestrial impact craters. Li et al. [25] also reported a challenge in distinguishing impact craters, volcanos, and valleys in deep learning for automatic crater detection because they share similar characteristics to the natural image.
The three types of impact crater, simple crater, complex crater, and impact crater basin, have distinctive topographic characteristics [2]. Typical fresh, simple impact craters have bowl-shaped depressions with a diameter of around 2 km for those formed in sedimentary rocks and about 4 km for crystalline rocks [8,26]. Fresh, complex impact craters also occur as circular or sub-circular topographic features with fresh central uplift peaks or concentric peak rings and a diameter greater than about 4 km. In addition, complex craters have a smaller depth-to-diameter ratio than simple craters [27,28]. Fresh impact crater basins have a relatively flat floor with one or more discrete terraces and the absence of sharp bowl-shaped topography and central uplift [28]. The impact basins have a diameter ranging from 3 km to 10 km [7].
Previous studies on the detection of impact craters using remote sensing have been focused on extraterrestrial impact craters, with few studies on terrestrial impact craters. This study aims to provide a reconnaissance survey method for the identification of possible terrestrial impact craters from global datasets. In view of the geometric characteristics of impact craters, DEMs can provide quantification of the geometric variables and, therefore, are the ideal data sources for reconnaissance surveys. For model development, this study collected 94 confirmed and well-preserved impact craters worldwide. Then, based on object attributes, a machine learning algorithm was tested for the classification of terrestrial impact craters from similarly shaped topography and commonly found land features such as volcanic calderas and valleys. The most important variables for differentiating impact craters from other terrestrial topographic features were identified using the machine learning algorithm. We expect this approach could contribute to the discovery of new impact craters with well-preserved topography.
2. Materials and Methods
To determine the optimal segmentation parameters for the detection of the exposed impact craters, the study investigated 94 confirmed exposed impact craters, including 24 simple, 66 complex, and 4 impact crater basins (~45% worldwide craters). The exposed impact craters can be defined as impact craters with a clear and easy-to-recognize circular morphology [7]. After the derivation of optimal segmentation parameters to define impact crater objects, object-based classification was carried out to test the detection of impact craters. In general, the morphology of impact craters can be defined as a circular depression bordered by an upraised rim formed by concentric normal faults and rebound of ejected materials [29]. We selected volcanic calderas to test classification efficiency with regard to features with similar topography. Volcanic calderas have circular depressions bordered by concentric or ring faults formed by the subsidence of the roof of the magma chamber due to the withdrawal of magma [30]. The ring faults of calderas exhibit geometrical properties similar to impact craters, such as diameter, topographic depression, and circular shape [31]. Therefore, this study included 104 volcanic calderas (44 single-pit and 60 coalesced-pit calderas) distributed over the world to test the detection efficiency of impact craters from similar topographic features (Figure 1, Appendix A, Table A2). Single-pit calderas are generally recognized by a single large depression, while coalesced-pit calderas are recognized by multiple interconnected depressions within a broader area [32,33,34]. This study also included valleys as an additional class for the classification model, which are common features associated with mountainous areas. We included 124 valleys to test the common features that can interfere with the detection of impact craters. The valleys have relatively shallow and wide depressions with gentle slope landforms, where the composite of valleys can form watersheds with various forms, such as fan or half-circle shapes [35,36].
2.1. Data Acquisition and Preparation
This study used the global Digital Elevation Model of Shuttle Radar Topography Mission data (SRTM DEM). DEM data with a resolution of 1 arcsec (~30 m/pixel) were downloaded from the USA Geological Survey (USGS) [37]. We used 94 confirmed impact craters because of the limited exposure of buried craters and the limited data coverage of the SRTM data [7] (Appendix A, Table A1). The buried impact craters are characterized by a lack of circular morphological expression on Earth’s surface due to post-impact geological activities such as weathering and erosion [2]. Additionally, 104 volcanic calderas with diameter sizes analogous to those of impact craters were selected from the Collapse Caldera Worldwide Database to test the detection models [38] (Figure 1, Appendix A, Figure A1). Moreover, 124 valley objects that are commonly found in mountainous areas were randomly selected during the segmentation and used for object-oriented classification to assess the detection efficiency of impact craters.
The original SRTM DEMs were filled to remove void pixels, and then the impact craters and volcanic calderas were clipped to generate a mosaic image including all target impact craters and other types of topographic features (Figure 2). The extent of the clipped area was approximately 3 times the apparent diameter size of target impact craters or calderas. Then, additional morphometric datasets, including slope, aspect, and hillshade layers, were derived from the DEM mosaic for object-oriented segmentation, following the findings from previous studies. These derivatives could enhance the circular topographic imprint of the rims that create a topographic contrast compared to the surrounding areas [20].
2.2. Optimum Scale Parameter Selection for Impact Crater Detection
In SRTM data, each crater consists of many 30-m pixels that define its geometries and topography. First, each crater should be extracted as an object with a clearly defined boundary. This study used a multi-resolution segmentation algorithm (MRS) implemented in eCognition developer 10.1 for the segmentation of the objects [39]. MRS employs a bottom-up region merging process of pixels. The segmentation starts by considering every pixel as a single object and accumulatively merging homogeneous pixels into distinct objects based on the scale parameter, shape and compactness, texture, and color. The merging process is stopped when the increase in homogeneity pixels exceeds the predefined scale in the eCognition software [20,40,41].
The segmentation results are mainly controlled by the Scale Parameter (SP) and associated shape and compactness [42], and, thus, different scale parameters would result in different sizes of segments. To determine the best scale parameters for optimal segmentation, the segmentation was iteratively conducted with changes in scale parameters from 5000 to 60 because of the size variability of impact craters and calderas (Figure 2). In this study, the shape of 0.4 and compactness of 0.6 were used for segmentation because they detected the circular rims effectively based on trial and error.
The topographical identification of the circumference of an impact crater is determined by the raised edge of the crater rim [7]. Based on this observation, we hypothesized that the circular topographic imprint of the rim on the DEM raster would induce the formation of homogeneous circular objects. Consequently, we employed a two-step assessment method to evaluate segmentation results. First, the segmentation results at each SP were subject to visual inspection to determine whether the segmentation was adequate to outline the impact crater rims. Then, the segmentation results that passed the inspection were used to calculate Area Fit Index (AFI). Area Fit Index (AFI) (Equation (1)) quantifies the fitness area between the reference area of impact craters (R) calculated from the Earth Impact Crater Database [2] and the area of impact crater object (S) derived from the segmentation.
An AFI equal to 0 is an ideal segmentation, and AFI values ≤ 0.5 are considered a good agreement [43,44,45,46]. The number of properly segmented impact craters and AFI have been summarized for each SP, and the SPs with the most impact crater segmentation and the minimum AFI were selected as optimum scale parameters. The morphometric characteristics of impact craters segmented at each SP were analyzed to determine the relationship between SPs and morphometric parameters.
2.3. Random Forest Model for Terrestrial Impact Crater Detection
The objects segmented at optimal SPs for terrestrial impact craters were further used for the object-oriented classification model. As a result of segmentation, 70 objects of impact craters, 85 objects of volcanic calderas, and 124 objects of valleys were used to develop a random forest-based detection model (RF hereafter). A total of 16 attributes were extracted from segmented objects and used for the RF model (Table 1). Among the 16 attributes, 7 topographic attributes described the morphological variation of the landform, 3 geometric attributes quantified the shape, and 6 texture attributes described the surface structure or pattern based on Gray–Level Co-occurrence Matrix (GLCM) (Table 1). To avoid overfitting, multicollinearity analysis between the 16 variables was performed. Based on the analysis, 5 variables with high intercorrelation were removed, including Angular Second Moment (ASM), contrast, entropy, aspect, and standard deviation of elevation, and 11 variables with low intercorrelation coefficient (<0.6) were used for the RF classification model.
RF is a nonparametric machine learning algorithm based on randomly generated tree classifiers, and the classification is through the ensemble majority votes. In addition, important variables that effectively define the relationship between predictors and target features can be quantitatively derived by Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) scores [47]. The RF algorithm was implemented in the R software. A total of 30% of samples were randomly selected for validation, and Ntrees and Mtry are 500 and 3, respectively. The variable importance of impact crater detection was rated based on Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) scores to determine the major contributor of morphometric parameters for impact crater detection. The roles of major contributors were discussed on how those variables could be used for terrestrial impact crater detection.
Table 1.
The attributes derived from detected objects.
| Type | Parameters | Brief Description | References |
|---|---|---|---|
| Topographic | Elevation | Express the height of an object below or above sea level | [48] |
| Slope | Express the steepness of the surface of an object | [49] | |
| Aspect | Express the orientation of slope of an object | [50] | |
| Hillshade | Illustrate the impression of the 3D surface of an object from the point of view of the sun | [50] | |
| Standard deviation of elevation | Express the variability of elevation within the object | [51] | |
| Standard deviation of slope | Express the variability of slope within the object | [51] | |
| Terrain Relief | Represent the difference between maximum and minimum elevation within the object | [52] | |
| Geometric | Length/Width ratio | Represent the relative comparison between the length and width of an object | [48] |
| Elliptical fit | Shape descriptor quantifies how much an area of an object fits the shape of an ellipse with a similar area | [48] | |
| Circularity | Shape descriptor that quantifies the roundness of an object: (Perimeter2)/(4π × Area) | [53] | |
| Texture (GLCM) | Homogeneity | Estimate the similarity between the pairs of pixels in the image object | [54] |
| Dissimilarity | Estimate the difference between the pairs of pixels in the image object | ||
| Angular Second Moment (ASM) | Estimate the amount of homogeneity or uniformity within the image object | ||
| Contrast | Measure the local intensity variation in the image object | ||
| Correlation | Measure the linear dependency between the pairs of pixel values in the image object | ||
| Entropy | Measure the unpredictability or randomness of the relationship between the pixels in the image object |
3. Results and Discussion
3.1. Selection of Optimal Scale Parameter for Impact Crater Segmentation
The visual inspection for segmentation results at each SP from 5000 to 60 revealed that the SP and diameter of segmented impact craters have positive relationships where the larger SPs would segment larger impact craters. Moreover, we found that SPs larger than 2000 would segment impact craters with an approximate diameter ≥70 km, and SPs lower or equal to 2000 would segment impact craters with a diameter < 70 km.
The SPs from 2000 to 5000 segmented only one impact crater from 5 impact craters in the 70–180 km diameter range (Table 2). The properly segmented impact crater was the Manicouagan impact crater (Canada), with a visible ring of ~70 km diameter. The SP 3000 best-segmented the possible rim or ring, whereas SP 5000 and 4000 under-segmented and 2000 over-segmented the outer rims (Figure 3a). In addition, an AFI value of 0.45 for the SP 3000 confirmed the good performance of segmentation. The rim of some craters, such as the Vredefort crater in South Africa, have experienced erosion, causing the rim boundaries to become less distinct. Consequently, the segmentation process was unable to detect the evident rim boundary in such cases (Figure 3b). Large craters with diameters greater than 70 km are rare on Earth’s surface and account for only about 5% of the samples. It might be attributed to Earth’s atmosphere, geological activities, erosion, and plate tectonics compared to the other planets and the Moon [2,55]. Therefore, we recommend SP 3000 as the optimal segmentation parameter for craters larger than 70 km.
Similarly, the segmentation results of the other SPs (2000, 1000, 700, 400, 200, 100, 80, 60) were evaluated, and the impact craters segmented from each SP varied because they vary in diameter and exposure level (Table 2, Figure 4). As a result, all SPs lower than 2000 except 60 have been selected as optimal scale parameters for impact crater segmentation. The diameter of segmented impact craters and 8 SPs showed a high correlation (R2) of 0.78 (Figure 4 and Figure 5).
The SP 200 properly segmented 21 out of 27 exposed impact craters with apparent diameters ranging from 1.8 to 12 km. The representative impact crater segmented by SP 200 is the Goat Paddock crater (Australia), 5 km in diameter (Figure 4). The highest detection in this scale was simply due to the large population of impact craters in the size range of terrestrial impact craters. Indeed, a previous study reported that almost half of terrestrial impact craters have a diameter range from 1 km to 10 km [7]. The smallest SP for impact crater segmentation was SP 80, which properly segmented 7 small-sized impact craters out of 8 with diameters in the range of 0.88–3.4 km. The scale mostly covered the smallest and most well-preserved simple impact craters, such as Tenoumer (Mauritania), Tswaing (South Africa), and Roter Kamm craters (Namibia). On the other hand, SP 60 over-segmented all 10 small impact craters of 0.024–0.64 km diameter. These small impact craters provided indiscernible topographic imprints from neighboring features on satellite data of 30 m resolution (Appendix A, Figure A2). Hence, SP 60 was not included as an optimal SP because it gave unreliable segmentation results for impact crater detection.
In general, there was a large overlap in segmented impact craters between the neighboring SPs (Figure 5). As a result of visual inspection, 8/11 SPs (3000, 2000, 1000, 700, 400, 200, 100, 80) provided the optimum homogeneous circular objects around the rim of 70/94 (74.5%) impact craters. The circular segments formed for well-preserved craters like Rotter Kamm (Namibia) and Vargeão Dome (Brazil) showed nearly zero AFI, about 0.06 and 0.04 (Figure 4). However, the goodness of fit for moderately eroded impact craters such as Charlevoix (Canada) and Ries (Germany) was relatively high, about 0.5 and 0.45, respectively (Figure 4). On the other side, 24/94 (25.5%) impact craters were not segmented properly due to the complete destruction of circular topographic imprints regardless of diameter. The impact craters Rochechouart (23 km, France) and Kelly West (6.6 km, Australia) belong to this group.
3.2. RF Classification of Terrestrial Impact Craters and Other Topographic Features
The terrestrial impact craters segmented from optimal SPs were compared with similar topographic features such as volcanic calderas and commonly found topographic features like valleys based on the RF model. Similarly, segmentation was conducted for those topographic features, and as a result, a total of 85 properly segmented volcanic calderas and 124 valleys were used for RF classification. The object attributes of terrestrial impact craters and other topographic features were extracted and used for input values in RF classification (Table 1). A total of 70% of objects (70 impact craters, 85 volcanic calderas, and 124 valleys) were used for the calibration model, and the remaining 30% were used for validation. The accuracy assessment showed that the classification model showed very good performance, with an overall accuracy of 88.4% and a kappa coefficient of 0.82. Notably, the model separated impact craters and calderas from valleys very effectively with an accuracy close to 100% (Table 3).
However, the accuracy for the detection of impact craters and volcanic calderas was relatively lower, with an accuracy of 74.1% and 88.5% due to the similarity in their topographic characteristics. Although the accuracy was the lowest for terrestrial impact craters, the accuracy of 78.7% is still considered good prediction accuracy, and thus, it infers that the remote sensing approaches to preliminary detection of undiscovered terrestrial impact craters can be effectively used.
The important variables for the detection of terrestrial impact craters were further derived from Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) indices (Figure 6). Based on the slope change shown by a sharp decrease in MDA and MDG, seven important variables were found: circularity, length-width ratio, relief, slope, elevation, homogeneity texture, and standard deviation of slope (Figure 6). Furthermore, violin plots visualized the distribution of the seven important variables of the three feature types based on kernel density estimation (Figure 7).
The plots presented a significant difference in median locations and the shape of the violin plots between the classes. The white dot points mark the median of the data along with the interquartile range, and the bulge in shape indicates higher probability density. Among the three attribute groups, it was concluded that geometric attributes play the most important role in the detection of terrestrial impact craters, followed by topographic and surface texture attributes (Figure 6 and Figure 7).
Circularity, a geometric attribute, was ranked as the most effective attribute for impact crater detection. Although both impact craters and volcanic calderas have circular shapes, they have differences in statistical distribution. For example, 75% of impact craters have circularity lower than 0.29, while 75% of volcanic calderas showed circularity higher than 0.22. A slight overlap was found in the range of 0.22–0.29 (Figure 7a). Meanwhile, valleys have distinctively lower circularity (Figure 7a). The circular shape of objects was further quantified by the length-to-width ratio. Half of the impact crater objects had a ratio higher than 1.5, and the majority of volcanic calderas (75%) were lower than 1.6. More than 75% of valleys showed a high ratio (>2.5) (Figure 7b).
In general, impact craters have jagged object boundaries, relatively lower circularity, and a higher length-to-width ratio compared to calderas. Indeed, prior studies reported that 34 impact craters have elliptical forms with a long-diameter axis to short-diameter axis ratio above 1.05 [7]. Moreover, oblique impactors, target heterogeneity, uneven erosion, and post-impact deformation have been presented as the main cause of elliptical form and modifications of the circular form of terrestrial impact crater rims [7]. The post-deformation processes include inward collapse and slump of rims, weathering, and erosion. Post-deformation modifies the shape of impact craters resulting in lower circularity and a high width-to-length ratio. The crater rims are composed of ejecta, melted, and brecciated materials, and thus, they are relatively vulnerable to erosion compared to crystalline igneous rocks. This phenomenon can also be confirmed by the ages of impact craters, which is related to exposed time for erosion and post-impact sedimentation in the central depression [56]. Indeed, impact crater objects with higher circularity (>0.72) were observed for 5 preserved simple impact craters, including Rotter Kamm, Worfe Creek, Meteor, Tenoumer, and Tswaing craters, which have ages of less than 5 Myrs [5]. In contrast, caldera rims have relatively higher circularity regardless of age because they are mainly formed by crystalline igneous rocks, which have higher resistance to erosion [57]. However, the circularity of caldera rims can be affected by characteristics of eruption, vents arrangement, and subsequent collapse. This study only used 104 volcanic calderas out of over 400 calderas worldwide [55,58]. Fully understanding the factors related to modification of the rim circularity needs more worldwide cases. Conversely, the valley objects were mostly characterized by elongated and asymmetrical shapes that could be associated with low circularity and a high length-to-width ratio.
Topographic attributes such as relief, slope, and elevation were also effective variables for differentiating terrestrial impact craters. The 75% of impact craters have relief less than 245 m, whereas 75% of volcanic calderas are higher than 192 m. The relief of valleys has a large overlap with impact craters, where 95% were lower than 192 m because they have similar depression (Figure 7c). The relatively lower relief of impact craters can be linked with shallow depression structures caused by ejecta and post-impact sediment deposition [10]. Indeed, Tsikals et al. [56] reported a decrease in relief of the central uplift ring and crater rims of 5 impact craters caused by sediment loading as a post-impact deformation. The shallower depression of impact craters was also found for lunar craters, which have a lower depth-to-diameter ratio [59]. On the other side, the relatively high internal relief found for volcanic calderas could be associated with a deep central depression formed by the collapse of the emptied magma chamber [60].
The slope attribute indicates that 75% of impact crater objects were mostly indicated by relatively gentle slopes less than 62°, while 50% of the volcanic caldera were mainly characterized by very steep rims with slopes greater than 60.2°. Valleys have distinctively low slopes of less than 49.6° (Figure 7d). Furthermore, a significant proportion of impact craters (88.5%) were found in elevations lower than 1000 m, while the majority (60%) of volcanic calderas are distributed in higher elevation ranges (1000–5233 m) (Figure 7e). The relatively lower elevation of impact craters can be explained by geological processes associated with the formation and post-deformation of impact craters. Impact craters are formed with circular rims formed by excavation and ejection, where the magnitude is controlled by the size and speed of the impacting object, the composition of the target material, and the angle of impact. This process induces relatively lower elevation than volcanoes, which create mountains. Moreover, the post-deformation of impact craters, such as collapse, slump of rims, and erosion, further reduces the elevation over time. The higher elevation of volcanic caldera can be explained by their topographic positioning, where most calderas are found in volcanic mountain summits that are relatively higher than most mountains. Volcanic calderas are mainly associated with magma eruption along tectonic boundaries, where most high mountain chains are distributed [61].
The GLCM homogeneity and standard deviation of slope showed a significant overlap for all three features compared to the other variables (Figure 7f,g). A substantial proportion (75%) of impact craters and volcanic calderas showed a homogeneity of less than 0.4 and 0.63, respectively. A relatively low homogeneity found for many impact craters could be the result of surface roughness caused by fractured, brecciated materials and central multi-structure rings [62]. Moreover, 75% of valleys were also characterized by homogeneity lower than 0.31 (Figure 7f). Similar patterns were also observed for the standard deviation of the slope variable, while volcanic calderas may have relatively higher variations in slope (Figure 7g).
4. Conclusions
This study is the first attempt to establish an optimal segmentation and object-oriented classification method based on confirmed and exposed terrestrial impact craters and DEM data. It confirmed that using remote sensing data, such as global digital elevation models, can effectively determine preliminary locations of undiscovered terrestrial impact craters around the world as a reconnaissance survey. Furthermore, this study not only tried to detect terrestrial impact craters but also detected volcanic calderas and tested the separability of other morphological features, such as valleys, from the impact craters. One limitation of our study is that the methods are only applicable to craters larger than 0.8 km in diameter. If smaller craters are to be detected, high-resolution DEMs are required, such as the TanDEM-X DEM by the German Aerospace Center. Moreover, this approach can only narrow down possible terrestrial impact crater candidates where topographic features are well-preserved. A more systematic geological analysis must follow up to validate the existence of terrestrial impact craters.
We introduced optimal scale parameters for the segmentation of terrestrial impact craters and detection of craters based on object-based attributes along with other terrestrial topographic features. The multi-resolution segmentation algorithm depicted the circular topographic imprints of impact craters and volcanic caldera rims from four morphometric layers derived from SRTM DEM data, including elevation, slope, aspect, and hillshade. Eight scale parameters ranging from 80 to 3000 were selected as the optimum scale parameters for terrestrial impact craters, which showed the goodness of fit area (0.01–0.5) and high correlation (R2 = 0.78) with diameters in the range of 0.88–100 km. Given the fact that terrestrial impact craters with wide diameters (>100 km) are rarely found (<5%) because of high atmospheric pressure [2], SP of 3000 could be the optimal segmentation parameter for large terrestrial impact craters. The smallest impact craters (0.024–0.64 km) are vulnerable to surface erosion [11] and have indiscernible topographic imprints with 30 m resolution data. Hence, the segmentation threshold for terrestrial impact craters with SRTM DEM is a diameter of 0.88 km which could be segmented by SP of 80, which is the minimum scale parameter suggested by our analysis.
The object-oriented classification using Random Forest successfully detected terrestrial impact craters as well as volcanic calderas and valleys, showing an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The detection accuracy of impact craters was the lowest, with 78.7% among the three topographic features. The relatively lower accuracy was associated with misclassification with volcanic caldera due to morphological similarity. The detection accuracy was highest for valleys (98.8%), followed by volcanic calderas (82.6%). Although the accuracy was lowest for terrestrial impact craters, the accuracy of 78.7% is still considered good prediction accuracy. This infers that this remote sensing approach to the preliminary detection of undiscovered terrestrial impact craters can be effectively used.
The important variables for the detection of terrestrial impact craters were identified as circularity, length-to-width ratio, relief, slope, and elevation. Terrestrial impact craters, in general, have relatively lower circularity and higher length-to-width ratios compared to volcanic calderas and significantly higher circularity and lower length-to-width ratios compared to valleys. Moreover, terrestrial impact craters have relatively lower relief, slope, and elevation than volcanic calderas and significantly higher relief, slope, and elevation than valleys. These geometric and topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters and volcanic calderas. The topography of impact craters surrounded by rims is formed by excavation and ejection and rebound and relaxation, and those processes induce relatively lower elevation. The post-deformation of impact craters, including inward collapse and slump of rims, post-impact erosion, and post-sediment deposition, lower the elevation, relief, and circularity with time. On the other hand, volcanic calderas are mostly associated with volcanic eruptions along tectonic boundaries where high mountain chains are distributed. Moreover, the deep central depression caused by the collapse of the emptied magma chamber results in relatively higher relief and slope. Compared to the impact crater rims, which are relatively unstable due to rapid excavation and the effects of the shock wave, the crystalline walls of volcanic calderas are more resistant to erosional processes, showing higher circularity and a lower width-to-length ratio. Moreover, the GLCM homogeneity of elevation could be included as a useful variable.
This study used 94 exposed and confirmed terrestrial impact craters (45% of worldwide impact craters) and incorporated similar topographic features, such as volcanic calderas, and the most common features, such as valleys. Given the fact that we have introduced a wide range of terrestrial impact craters from a diameter size of 0.88 to 100 km, the method can be generalized to the detection of unidentified craters worldwide using the global DEM models as a reconnaissance survey method. The segmentation parameters and detection variables introduced in this study could significantly contribute to the discovery of hidden terrestrial impact crater candidates in developing countries and remote areas.
Author Contributions
Methodology, H.E. and J.Y.; Software, H.E.; Validation, H.E.; Formal analysis, H.E. and J.Y.; Investigation, H.E. and J.Y.; Resources, J.Y.; Writing—original draft, H.E. and J.Y.; Writing—review & editing, J.Y. and L.W.; Visualization, H.E.; Supervision, J.Y. and D.E.R.R.; Project administration, J.Y. and S.H.C.; Funding acquisition, S.H.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by [National Research Foundation of Korea] grant number [RS-2023-00208476] and [NRF-2021R1A4A5026233].
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
The confirmed and exposed terrestrial impact craters used in this study were collected from an encyclopedic atlas of terrestrial impact craters [2,7].
| Impact Crater | Country | Latitude | Longitude | ID | Diameter (km) | Exposure | Target Lithology | Type | Ages (ma) |
|---|---|---|---|---|---|---|---|---|---|
| Dhala | India | 25.298 | 78.142 | I01 | 12 | ex, pc | Crystalline | C | 1700–2500 |
| Sierra Madera | USA | 30.596 | −102.912 | I02 | 12 | ex, pc | Sandstone | C | 100 |
| Gweni-Fada | Chad | 17.421 | 21.755 | I03 | 14–22 | ex, pc | Sandstone | C | 355 |
| Bigach | Kazakhstan | 48.568 | 82.036 | I04 | 8 | ex, pc | Mixed | C | 3–5 |
| Meteor Crater | USA | 35.027 | −111.023 | I05 | 1.2 | ex, pc | Sandstone | S | 0.05 |
| Ramgarh | India | 25.335 | 76.624 | I06 | 10.2 | ex, pc | Sandstone | C | 165 |
| Cerro do Jarao | Brazil | −30.211 | −56.539 | I07 | 13 | ex, smor | Sandstone | C | 137 |
| Connolly Basin | Australia | −23.538 | 124.761 | I08 | 9 | ex, pc | Sandstone | C | 55–75 |
| Tenoumer | Mauritania | 22.918 | −10.405 | I09 | 1.9 | ex, pc | Mixed | S | 1.52 |
| Piccaninny | Australia | −17.420 | 128.438 | I10 | 7 | ex, smor | Sandstone | C | 360 |
| Chogye | SouthKorea | 35.537 | 128.269 | I11 | 7 | ex, pc | Sandstone | C | 0.03–0.06 |
| Vargeao Dome | Brazil | −26.805 | −52.164 | I12 | 12.4 | ex | Sandstone | C | 137 |
| Mien | Sweden | 56.431 | 14.856 | I13 | 9 | ex, sub | Crystalline | C | 118.7 |
| Gow | Canada | 56.453 | −104.482 | I15 | 5 | ex, sub | Crystalline | C | 250 |
| Santa Marta | Brazil | −10.167 | −45.233 | I16 | 10 | ex, pc | Sandstone | C | 93 |
| Acraman | Australia | −32.017 | 135.450 | I17 | 40–85 | ex, sub | Crystalline | C | 580 |
| Gosses Bluff | Australia | −23.817 | 132.308 | I18 | 22 | ex, pc | Sandstone | C | 142 |
| Upheaval Dome | USA | 38.433 | −109.928 | I20 | 6 | ex | Sandstone | C | 66–100 |
| Foelsche | Australia | −16.676 | 136.784 | I21 | 6 | ex, pc | Sandstone | C | 541–981 |
| Jebel Waqf as Suwwan | Jordan | 31.039 | 36.807 | I22 | 6.1 | ex, pc | Sandstone | C | 37 |
| Agoudal | Morocco | 31.996 | −5.516 | I24 | 2.8 | ex | Sandstone | C | 0.3 |
| Aorounga | Chad | 19.084 | 19.244 | I25 | 12.6–16 | ex | Sandstone | C | 355 |
| Decaturville | USA | 37.890 | −92.720 | I26 | 6 | ex, pc | Sandstone | C | 300 |
| Bosumtwi | Ghana | 6.500 | −1.408 | I27 | 10.5 | ex, sub | Mixed | C | 1.07 |
| Decorah | USA | 43.300 | −91.772 | I29 | 5.6 | pc | Sandstone | T | 460–483 |
| Ouarkziz | Algeria | 29.004 | −7.551 | I30 | 3.5 | ex, pc | Sandstone | T | 66 |
| Zhamanshin | Kazakhstan | 48.350 | 60.937 | I32 | 14 | ex, pc | mixed | C | 0.75–1.1 |
| Oasis | Libya | 24.572 | 24.412 | I33 | 5.2–18 | ex, pc | Sandstone | C | 120 |
| Serra da Cangalha | Brazil | −8.082 | −46.857 | I34 | 13.7 | ex | Crystalline | C | 300 |
| La Moinerie | Canada | 57.440 | −66.586 | I35 | 8 | ex, sub | Crystalline | C | 400 |
| Middlesboro | USA | 36.631 | −83.728 | I36 | 6 | ex | Sandstone | C | 290–300 |
| Colonia | Brazil | −23.880 | −46.706 | I37 | 3.6 | ex, pc | Crystalline | T | 5–36 |
| Vista-Alegre | Brazil | −25.961 | −52.690 | I38 | 9.5 | ex | Mixed | C | 111–134 |
| B.P. structure | Libya | 25.318 | 24.310 | I40 | 3.4 | ex | Sandstone | C | 120 |
| Ragozinka | Russia | 58.706 | 61.797 | I41 | 9 | ex, pc | mixed | C | 50 |
| Goyder | Australia | −13.477 | 135.040 | I43 | 3 | ex, pc | Sandstone | C | 150–1400 |
| Chiyli | Kazakhstan | 49.177 | 57.834 | I44 | 5.5 | ex, pc | Sandstone | C | 5.5 |
| Lonar | India | 19.974 | 76.509 | I46 | 1.88 | ex, sub | Crystalline | S | 0.57 |
| Wetumpka | USA | 32.525 | −86.176 | I47 | 7 | ex | Crystalline | C | 84 |
| Karakul | Tajikistan | 39.067 | 73.433 | I48 | 52 | ex, smor | Mixed | C | 50–5 |
| Pantasma | Nicaragua | 13.365 | −85.954 | I49 | 14 | ex | Crystalline | C | 0.8 |
| Shunak | Kazakhstan | 47.207 | 72.761 | I50 | 2.8 | ex, pc | Crystalline | S | 34 |
| Deep Bay | Canada | 56.415 | −102.983 | I51 | 13 | ex, sub | Crystalline | C | 95–102 |
| Crawford | Australia | −34.728 | 139.033 | I53 | 8.5 | ex | Crystalline | C | 32–38 |
| Talemzane | Algeria | 33.315 | 4.034 | I54 | 1.75 | ex, pc | Sandstone | S | 0.5–3 |
| Xiuyan | China | 40.364 | 123.460 | I57 | 1.8 | ex, pc | Crystalline | S | 0.05 |
| Goat Paddock | Australia | −18.348 | 126.677 | I61 | 5 | ex, pc | Sandstone | T | 56–64 |
| Tin Bider | Algeria | 27.600 | 5.112 | I62 | 6 | ex, pc | Sandstone | C | 50 |
| Clearwater West | Canada | 56.211 | −74.500 | I63 | 32 | ex, sub | Mixed | C | 290–300 |
| Clearwater East | Canada | 56.064 | −74.083 | I64 | 24 | ex, sub | Mixed | C | 460–470 |
| Spider | Australia | −16.742 | 126.089 | I66 | 13 | ex, smor | Sandstone | C | 573 |
| Roter Kamm | Namibia | −27.762 | 16.289 | I67 | 2.5 | ex, pc | Sandstone | S | 5 |
| Yilan | China | 46.391 | 129.311 | I69 | 1.85 | ex, pc | Crystalline | S | 0.05 |
| Tswaing | SouthAfrica | −25.411 | 28.083 | I70 | 1.13 | ex, sub | Mixed | S | 0.22 |
| Shoemaker | Australia | −25.881 | 120.883 | I71 | 30 | ex, pc | Sandstone | C | 1630 |
| Brent | Canada | 46.078 | −78.482 | I73 | 3.8 | ex, pc | Crystalline | S | 396–453 |
| Ries | Germany | 48.873 | 10.695 | I77 | 26 | ex, pc | Crystalline | C | 15 |
| Wolfe creek | Australia | −19.170 | 127.795 | I78 | 0.88 | ex, pc | Sandstone | S | 0.12 |
| Cleanskin | Australia | −18.170 | 137.942 | I79 | 15 | ex, pc | Sandstone | C | 540–1400 |
| Luizi | D.R. Congo | −10.175 | 28.006 | I65 | 17 | ex | Sandstone | C | 575 |
| Carswell | Canada | 58.418 | −109.517 | I82 | 39 | ex, smor | Mixed | C | 481 |
| Strangways | Australia | −15.200 | 133.567 | I84 | 25–40 | ex, pc | Mixed | C | 646 |
| Sao Miguel do Tapuio | Brazil | −5.617 | −41.388 | I87 | 21 | ex | Sandstone | C | 120 |
| Amelia Creek | Australia | −20.858 | 134.883 | I88 | 20 | ex | Mixed | C | 600–1600 |
| Mistastin | Canada | 55.891 | −63.311 | I89 | 28 | ex, sub | Crystalline | C | 36.6 |
| Charlevoix | Canada | 47.533 | −70.350 | I90 | 55 | ex, sub | Mixed | C | 450 |
| Beaverhead | USA | 44.600 | −112.967 | I91 | 60–75 | ex, pc | Mixed | C | 600 |
| Araguainha | Brazil | −16.785 | −52.983 | I92 | 40 | ex | Crystalline | C | 252–259 |
| Lawn Hill | Australia | −18.693 | 138.652 | I93 | 20 | ex, pc | Sandstone | C | 472 |
| Manicouagan | Canada | 51.399 | −68.683 | I94 | 70–100 | ex, sub | Crystalline | C | 214 |
| Liverpool | Australia | −12.393 | 134.047 | I81 | 2 | ex, pc | Sandstone | S | 150 |
| Tabun-Khara obo | Mongolia | 44.131 | 109.654 | I83 | 1.3 | ex, pc | Sandstone | S | 145–163 |
| Ritland | Norway | 59.249 | 6.422 | I39 | 2.7 | ex, pc | mixed | S | 500–540 |
| Aouelloul | Mauritania | 20.241 | −12.675 | I45 | 0.39 | ex, pc | Sandstone | S | 3.1 |
| Dalgaranga | Australia | −27.633 | 117.289 | I56 | 0.024 | ex, pc | Crystalline | S | 0.27 |
| Monturaqui | Chile | −23.928 | −68.262 | I58 | 0.36 | ex | Crystalline | S | 0.663 |
| Kalkkop | SouthAfrica | −32.709 | 24.432 | I55 | 0.64 | ex, pc | Sandstone | S | 0.25 |
| Amguid | Algeria | 26.088 | 4.395 | I68 | 0.45 | ex, pc | Sandstone | S | 0.1 |
| Kamil | Egypt | 22.018 | 26.088 | I19 | 0.045 | ex | Sandstone | S | 0.003 |
| Boxhole | Australia | −22.613 | 135.196 | I14 | 0.17 | ex | Crystalline | S | 0.017 |
| Whitecourt | Canada | 53.999 | −115.596 | I72 | 0.036 | ex | Sandstone | S | 0.001 |
| Henbury | Australia | −24.571 | 133.148 | I23 | 0.18 | ex, pc | Sandstone | S | 0.0042 |
| Rio Cuarto | Argentina | −32.871 | −64.183 | I31 | 4.5 | ex | Sandstone | S | 0.11 |
| Yallalie | Australia | −30.443 | 115.771 | I59 | 12 | pc | Sandstone | C | 83.6–89.8 |
| Presquile | Canada | 49.726 | −74.833 | I74 | 22 | ex, sub | Crystalline | C | 500 |
| Macha | Russia | 60.085 | 117.652 | I60 | 0.3 | ex, pc, | Sandstone | C | 0.0073 |
| Rock Elm | USA | 44.717 | −92.228 | I28 | 6.5 | ex, pc | Sandstone | C | 410–460 |
| Mount Toondina | Australia | −27.945 | 135.359 | I52 | 4 | ex, pc | Sandstone | C | 66–144 |
| Kelly west | Australia | −19.933 | 133.950 | I75 | 14 | ex, pc | Sandstone | C | 541 |
| Matt Wilson | Australia | −15.506 | 131.181 | I42 | 7.5 | ex | Sandstone | C | 1400–1500 |
| Sudbury | Canada | 46.600 | −81.183 | I76 | 180–200 | ex, pc | Crystalline | C | 1849 |
| Vredefort | SouthAfrica | −27.009 | 27.500 | I85 | 180–275 | ex, pc | Crystalline | C | 2023 |
| Rochechouart | France | 45.831 | 0.782 | I86 | 23 | ex | Crystalline | C | 201 |
| Yarrabubba | Australia | −27.183 | 118.833 | I80 | 30 | ex, pc | Sandstone | C | 2246 |
C: Complex, S: Simple, T: Transitional, ex: exposed, pc: partial covered, smor: subdued morphology, sub: submerged.
Table A2.
Volcanic calderas used in this study were selected from collapse caldera worldwide database (CCDB) [38].
Table A2.
Volcanic calderas used in this study were selected from collapse caldera worldwide database (CCDB) [38].
| Volcanic Calderas | Country | Latitude | Longitude | D_max | D_min | Type | ID |
|---|---|---|---|---|---|---|---|
| Toba | Indonesia | 2.580 | 98.830 | 100 | 30 | C | V103 |
| Taal | Philippines | 14.010 | 120.998 | 30 | 25 | S | V10 |
| Kawah Ijen | Indonesia | −8.119 | 114.056 | 20 | 20 | S | V18 |
| Ijen_II | Indonesia | −8.058 | 114.244 | 18 | 17 | S | V19 |
| Shikotsu | Japan | 42.751 | 141.317 | 15 | 13 | S | V49 |
| Long Valley | USA | 37.717 | −118.884 | 32 | 18 | C | V56 |
| Solitario | USA | 29.451 | −103.809 | 16 | 16 | C | V62 |
| Rotorua | NewZeland | −38.080 | 176.250 | 20 | 16 | S | V73 |
| Crater Lake | USA | 42.930 | −122.113 | 10 | 8 | S | V74 |
| Henry’s Fork Caldera | USA | 44.330 | −111.330 | 37 | 29 | C | V77 |
| Ngorongoro | Tanzania | −3.177 | 35.580 | 19 | 16 | S | V80 |
| Kapenga | NewZeland | −38.089 | 176.273 | ? | ? | C | V92 |
| Colli Albani | Italy | 41.754 | 12.700 | 12 | 10 | C | V97 |
| Copahue | Chile & Argentine | −37.858 | −71.177 | 10 | 10 | S | V03 |
| Paektu Mountain | China & N. Korea | 42.005 | 128.056 | 14 | 12 | S | V07 |
| Karymshina | Russia | 54.118 | 159.657 | 25 | 15 | C | V101 |
| Aso | Japan | 32.885 | 131.084 | 25 | 18 | C | V17 |
| Mount Longonot | Kenya | −1.155 | 36.354 | 12 | 8 | C | V37 |
| Valles | USA | 35.870 | −106.570 | 22 | 16 | C | V58 |
| Braciano | Italy | 42.316 | 12.174 | 20 | 15 | S | V96 |
| Akademia Nauk | Russia | 53.981 | 159.462 | 11 | 11 | S | V98 |
| Uzon | Russia | 54.500 | 159.970 | 12 | 9 | C | V99 |
| Ayarza | Guatemala | 14.420 | −90.120 | 7 | 5 | S | V08 |
| Mount Okmok | USA | 53.468 | −168.175 | 9.3 | 9.3 | C | V09 |
| Deriba | Sudan | 12.950 | 24.270 | 5 | 5 | C | V23 |
| Toya | Japan | 42.598 | 140.856 | 10 | 9 | C | V48 |
| Mount Silali | Kenya | 1.152 | 36.231 | 8 | 5 | C | V59 |
| Ilopango | El Salvador | 13.670 | −89.050 | 11 | 8 | C | V60 |
| Alcedo | Ecuador | −0.430 | −91.120 | 7.4 | 6.1 | S | V67 |
| Mount Aniakchak | USA | 56.864 | −158.151 | 10 | 10 | C | V79 |
| Emi Koussi | Chad | 19.851 | 18.538 | 16 | 12 | C | V93 |
| Huichapan | Mexico | 20.340 | −99.550 | 10 | 10 | C | V104 |
| Sollipulli | Chile | −38.970 | −71.520 | 4 | 4 | S | V15 |
| Gadamsa | Ethiopia | 8.356 | 39.181 | 7 | 9 | C | V24 |
| Karkar | New Guinea | −4.650 | 145.967 | 5.5 | 3.2 | C | V25 |
| Agua de Pau | Portugal | 37.770 | −25.470 | 7 | 4 | S | V26 |
| The Barrier | Kenya | 2.320 | 36.587 | 6 | 5 | C | V29 |
| Mallahle | Ethiopia | 13.270 | 41.650 | 6 | 6 | C | V34 |
| Asavyo | Ethiopia | 13.098 | 41.599 | 12 | 12 | C | V35 |
| Suswa | Kenya | −0.915 | 36.457 | 12 | 8 | C | V36 |
| Kone | Ethiopia | 8.840 | 39.688 | 6 | 5 | C | V40 |
| San Pedro | Mexico | 21.263 | −104.698 | 8 | 8 | C | V46 |
| Gallosuelo | NewZeland | −5.200 | 151.240 | ? | ? | S | V51 |
| Darwin | Ecuador | −0.180 | −91.280 | 5 | 5 | S | V68 |
| Cerro Azul | Ecuador | −0.170 | −91.240 | 5 | 5 | S | V69 |
| Sierra Negra | Ecuador | −0.830 | −91.170 | 10 | 7 | S | V71 |
| Worf | Ecuador | −0.020 | −91.350 | 7 | 5 | S | V72 |
| Cerro Panizos | Argentina | −22.187 | −66.681 | 15 | 15 | C | V75 |
| Gadamsa | Ethiopia | 8.350 | 39.180 | 10 | 8 | C | V76 |
| Incapillo | Argentina | −27.902 | −68.824 | 6 | 5 | C | V78 |
| Olmoti | Tanzania | −3.016 | 35.652 | 6.5 | 6.5 | C | V82 |
| Nemurt | Turkey | 38.621 | 42.235 | 8.5 | 7 | S | V91 |
| Vico | Italy | 42.120 | 12.230 | - | - | C | V94 |
| Montefiascone | Italy | 42.579 | 11.931 | 3 | 3 | S | V95 |
| Laslajas | Nicaragua | 12.300 | −85.730 | 7 | 7 | C | V04 |
| Krasheninnikov | Russia | 54.593 | 160.273 | 11 | 9 | C | V102 |
| Karthala | Comores Island | −11.760 | 43.353 | 4 | 3 | S | V12 |
| Ale Bagu | Ethiopia | 13.508 | 40.632 | 3 | 2.1 | C | V13 |
| Amealco | Mexico | 20.126 | −100.169 | 11 | 11 | C | V16 |
| Numazawa | Japan | 37.450 | 139.579 | 3 | 2 | C | V22 |
| Sete Cidades | Portugal | 37.870 | −25.780 | 5 | 5 | S | V28 |
| Fantale | Ethiopia | 8.984 | 39.907 | 4 | 3 | S | V39 |
| Mauna Loa | USA | 19.479 | −155.603 | 6.2 | 2.5 | C | V41 |
| Fernandina | Ecuador | −0.370 | −91.550 | 6.5 | 6.5 | S | V70 |
| Embagai | Tanzania | −2.911 | 35.827 | 4 | 4 | S | V81 |
| Gorely Khrebet | Russia | 52.558 | 158.027 | 13 | 10 | C | V87 |
| Changbaishan | China & N. Korea | 42.005 | 128.058 | 5 | 5 | S | V05 |
| Mount Katmai | USA | 58.260 | −154.975 | 10 | 10 | S | V11 |
| Towada IV | Japan | 40.500 | 140.900 | 3.5 | 3 | S | V21 |
| Furnas | Portugal | 37.770 | −25.320 | 6 | 6 | C | V27 |
| Kaguyak | USA | 58.613 | −154.053 | 3 | 2.5 | S | V31 |
| Mazama | USA | 58.613 | −154.053 | 10 | 8 | C | V38 |
| Villarrica | Chile &Argentine | −39.420 | −71.950 | 9 | 6 | C | V53 |
| Aoba (Ambae) | Vanuatu | −15.389 | 167.835 | 2.1 | 2.1 | S | V02 |
| Izu-Oshima | Japan | 34.724 | 139.394 | 4.5 | 3.5 | C | V06 |
| Ngozi | Tanzania | −9.010 | 33.554 | 3 | 3 | S | V30 |
| Pinatubo | Philippines | 15.142 | 120.350 | 2.5 | 2.5 | S | V32 |
| Cerro Azul | Chile | −35.653 | −70.761 | 4 | 5 | S | V33 |
| Geger Halang | Indonesia | −6.896 | 108.408 | 4 | 4 | C | V44 |
| Ceboruco | Mexico | 21.125 | −104.508 | 3.7 | 3.7 | C | V45 |
| Mount Meru | Tanzania | −3.247 | 36.748 | 3.5 | 3.5 | C | V52 |
| Ikeda | Japan | 31.237 | 130.561 | 5 | 4 | C | V57 |
| Coate peque | El Salvador | 13.859 | −89.553 | 5 | 5 | C | V61 |
| TianChi | China | 42.007 | 128.054 | 5 | 5 | S | V64 |
| Tazawa | Japan | 39.721 | 140.663 | 6 | 6 | S | V65 |
| Sakurajima | Japan | 31.578 | 130.661 | 23 | 17 | C | V01 |
| Conguillio | Chile | −38.901 | −71.728 | 4 | 3 | S | V14 |
| Paka | Kenya | 0.918 | 36.191 | 1.5 | 1.5 | S | V20 |
| Mauna Kea | USA | 19.813 | −155.472 | 4.2 | 2.5 | C | V42 |
| Poas | Costa Rica | 10.200 | −84.233 | 2.5 | 2.5 | S | V43 |
| Kuttara | Japan | 42.500 | 141.180 | 3 | 3 | S | V47 |
| Gallosuelo | NewZeland | −5.342 | 151.117 | 13 | 10.5 | C | V50 |
| Logo Tromen | Chile-Argentine | −39.931 | −72.028 | 5 | 6 | C | V54 |
| Mocho chosheunco | Chile-Argentine | −39.500 | −71.715 | 5 | 4 | C | V55 |
| Lake City | USA | 37.955 | −107.391 | 18 | 15 | C | V63 |
| Groppo | Ethiopia | 11.715 | 40.232 | 3.8 | 3.8 | C | V66 |
| Monduli | Tanzania | −2.868 | 35.949 | 2.9 | 2.9 | S | V83 |
| Gela | Tanzania | −2.763 | 35.916 | 3.1 | 3.1 | S | V84 |
| Oldeani | Tanzania | −3.297 | 35.449 | 3 | 3 | S | V85 |
| Creede | USA | 37.748 | −106.922 | 25 | 20 | C | V86 |
| Mutnovsky | Russia | 52.451 | 158.166 | 9 | 9 | C | V88 |
| Ksudach | Russia | 51.800 | 157.530 | 7 | 7 | C | V89 |
| Opala | Russia | 52.543 | 157.339 | 14 | 12 | C | V90 |
| Mary Semyachik | Russia | 54.058 | 159.442 | 10 | 10 | C | V100 |
C: Coalesced pit, S: Single pit, D_min: Minimum diameter, D_max: Maximum diameter.
Figure A1.
Size frequency distribution plot of impact craters and volcanic calderas used in this study.
Figure A1.
Size frequency distribution plot of impact craters and volcanic calderas used in this study.
Figure A2.
Representative segmentation of impact craters smaller than 0.88 km diameter showing unreliable segmentation results.
Figure A2.
Representative segmentation of impact craters smaller than 0.88 km diameter showing unreliable segmentation results.
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Figure 1.
World map with the exposed impact craters and a sample of volcanic calderas locations.
Figure 2.
The graphical workflow for object-oriented terrestrial impact detection.
Figure 3.
DEM layers of (a) Manicouagan impact crater (Canada); and (b) Vredefort crater (South Africa) with their respective multi scales segmentation results. Red segments represent the objects intended to suitably outline the possible crater rim at different SPs, and blue segments represent the various objects formed around other features.
Figure 3.
DEM layers of (a) Manicouagan impact crater (Canada); and (b) Vredefort crater (South Africa) with their respective multi scales segmentation results. Red segments represent the objects intended to suitably outline the possible crater rim at different SPs, and blue segments represent the various objects formed around other features.
Figure 4.
Multiple scale segmentation results of representative impact craters.
Figure 5.
The overlapping situation for each scale impact craters; the blue dot points represent the impact craters that were firstly detected at larger SPs, and orange dot points represent the impact craters that were re-detected at other subscale scales.
Figure 5.
The overlapping situation for each scale impact craters; the blue dot points represent the impact craters that were firstly detected at larger SPs, and orange dot points represent the impact craters that were re-detected at other subscale scales.
Figure 6.
Variable importance of object attributes, the 7 variables above the red dotted line showed the major influence for detecting impact craters.
Figure 6.
Variable importance of object attributes, the 7 variables above the red dotted line showed the major influence for detecting impact craters.
Figure 7.
The violin plots for crater, caldera, and valley showing the density distribution of seven important variables: (a) Circularity index, (b) Lenth to width ratio index, (c) relief, (d) slope, (e) elevation, (f) GLCM Homogeneity and (g) standard deviation of slope.
Figure 7.
The violin plots for crater, caldera, and valley showing the density distribution of seven important variables: (a) Circularity index, (b) Lenth to width ratio index, (c) relief, (d) slope, (e) elevation, (f) GLCM Homogeneity and (g) standard deviation of slope.
Table 2.
Attempted optimum scale parameters and number of detected features.
| Segmentation Settings | Impact Craters | |||||
|---|---|---|---|---|---|---|
| SP | Shape | Compactness | Diameter Range (km) | Count Segmented (%) | AFI | Count Not Segmented (%) |
| 5000–3000 | 0.4 | 0.6 | 70–180 | 1 (1.0) | 0.48 | 2 (2.1) |
| 2000 | 0.4 | 0.6 | 24–60 | 8 (8.5) | 0.07–0.50 | 0 |
| 1000 | 0.4 | 0.6 | 8–39 | 12 (12.7) | 0.01–0.41 | 0 |
| 700 | 0.4 | 0.6 | 6–30 | 6 (6.3) | 0.08–0.43 | 2 (2.1) |
| 400 | 0.4 | 0.6 | 6–17 | 9 (9.5) | 0.04–0.43 | 3 (3.1) |
| 200 | 0.4 | 0.6 | 1.8–12 | 21 (22.3) | 0.08–0.49 | 6 (6.3) |
| 100 | 0.4 | 0.6 | 1.8–6 | 6 (6.3) | 0.12–0.44 | 0 |
| 80 | 0.4 | 0.6 | 0.88–3.4 | 7 (7.4) | 0.12–0.35 | 1 |
| 60 | 0.4 | 0.6 | 0.024–0.64 | 0 | 0 | 10 (11.7) |
| Overall: | 70/94 (74.5%) | 24 (25.5%) | ||||
Table 3.
Confusion matrix of training, validation data from RF algorithm, where Production’s Accuracy (PA) and User’s Accuracy (UA) for each class are also presented.
Table 3.
Confusion matrix of training, validation data from RF algorithm, where Production’s Accuracy (PA) and User’s Accuracy (UA) for each class are also presented.
| Training data | |||||
| Classes: | Crater | Caldera | Valley | ||
| Crater | 43 | 0 | 0 | Accuracy | 100% |
| Caldera | 0 | 59 | 0 | Kappa Coefficient | 1 |
| Valley | 0 | 0 | 82 | ||
| Validation data | |||||
| Classes: | Crater | Caldera | Valley | PA (%) | UA (%) |
| Crater | 20 | 3 | 1 | 74.1 | 83.3 |
| Caldera | 7 | 23 | 0 | 88.5 | 76.6 |
| Valley | 0 | 0 | 41 | 97.6 | 100 |
| Overall accuracy | |||||
| Accuracy | 88.4 | ||||
| Kappa coefficient | 0.82 | ||||
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Emmanuel, H.; Yu, J.; Wang, L.; Choi, S.H.; Rwatangabo, D.E.R. Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey. Remote Sens. 2023, 15, 3807. https://doi.org/10.3390/rs15153807
AMA Style
Emmanuel H, Yu J, Wang L, Choi SH, Rwatangabo DER. Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey. Remote Sensing. 2023; 15(15):3807. https://doi.org/10.3390/rs15153807
Chicago/Turabian StyleEmmanuel, Habimana, Jaehyung Yu, Lei Wang, Sung Hi Choi, and Digne Edmond Rwabuhungu Rwatangabo. 2023. "Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey" Remote Sensing 15, no. 15: 3807. https://doi.org/10.3390/rs15153807
APA StyleEmmanuel, H., Yu, J., Wang, L., Choi, S. H., & Rwatangabo, D. E. R. (2023). Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey. Remote Sensing, 15(15), 3807. https://doi.org/10.3390/rs15153807
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